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Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment.

ABSTRACT: Subgingival microbiome dysbiosis promotes the development of periodontitis, an irreversible chronic inflammatory disease associated with metabolic diseases. However, studies regarding the effects of a hyperglycemic microenvironment on host-microbiome interactions and host inflammatory response during periodontitis are still scarce. Here, we investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and transcriptome of a gingival coculture model stimulated with dysbiotic subgingival microbiomes. HGF-1 cells overlaid with U937 macrophage-like cells were stimulated with subgingival microbiomes collected from four healthy donors and four patients with periodontitis. Pro-inflammatory cytokines and matrix metalloproteinases were measured while the coculture RNA was submitted to a microarray analysis. Subgingival microbiomes were submitted to 16s rRNA gene sequencing. Data were analyzed using an advanced multi-omics bioinformatic data integration model. Our results show that the genes krt76, krt27, pnma5, mansc4, rab41, thoc6, tm6sf2, and znf506 as well as the pro-inflammatory cytokines IL-1β, GM-CSF, FGF2, IL-10, the metalloproteinases MMP3 and MMP8, and bacteria from the ASV 105, ASV 211, ASV 299, Prevotella, Campylobacter and Fretibacterium genera are key intercorrelated variables contributing to periodontitis-induced inflammatory response in a hyperglycemic microenvironment. In conclusion, our multi-omics integration analysis unveiled the complex interrelationships involved in the regulation of periodontal inflammation in response to a hyperglycemic microenvironment.

SUBMITTER: Lafleur S

PROVIDER: S-EPMC10218861 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment.

Lafleur Sarah S Bodein Antoine A Mbuya Malaïka Mutombo Joanna J Mathieu Alban A Joly Beauparlant Charles C Minne Xavier X Chandad Fatiha F Droit Arnaud A Houde Vanessa P VP

International journal of molecular sciences 20230516 10

Subgingival microbiome dysbiosis promotes the development of periodontitis, an irreversible chronic inflammatory disease associated with metabolic diseases. However, studies regarding the effects of a hyperglycemic microenvironment on host-microbiome interactions and host inflammatory response during periodontitis are still scarce. Here, we investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and transcriptome of a gingival coculture model stimulated with dys ...[more]

PMID: 37240180

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Project description:Chronic periodontitis is an inflammatory disease of the periodontium affecting nearly 65 million adults in the United States. Changes in subgingival microbiota have long been associated with chronic periodontitis. Recent culture-independent molecular studies have revealed the immense richness and complexity of oral microbial communities. However, data sets across studies have not been directly compared, and whether the observed microbial variations are consistent across different studies is not known. Here, we used 16S rRNA sequencing to survey the subgingival microbiota in 25 subjects with chronic periodontal disease and 25 healthy controls and compared our data sets with those of three previously reported microbiome studies. Consistent with data from previous studies, our results demonstrate a significantly altered microbial community structure with decreased heterogeneity in periodontal disease. Comparison with data from three previously reported studies revealed that subgingival microbiota clustered by study. However, differences between periodontal health and disease were larger than the technical variations across studies. Using a prediction score and applying five different distance metrics, we observed two predominant clusters. One cluster was driven by Fusobacterium and Porphyromonas and was associated with clinically apparent periodontitis, and the second cluster was dominated by Rothia and Streptococcus in the majority of healthy sites. The predicted functional capabilities of the periodontitis microbiome were significantly altered. Genes involved in bacterial motility, energy metabolism, and lipopolysaccharide biosynthesis were overrepresented in periodontal disease, whereas genes associated with transporters, the phosphotransferase system, transcription factors, amino acid biosynthesis, and glycolysis/gluconeogenesis were enriched in healthy controls. These results demonstrate significant alterations in microbial composition and function in periodontitis and suggest genes and metabolic pathways associated with periodontal disease.

Project description:An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various "Gini" importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from -6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were Treponema denticola, Mogibacterium timidum, Fretibacterium spp., and Tannerella forsythia, while Actinomyces naeslundii and Streptococcus sanguinis were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera (Treponema, Fretibacterium, and Actinomyces) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.

Project description:Crohn's disease (CD) is a chronic inflammatory bowel disease. An imbalanced microbiome (dysbiosis) can predispose to many diseases including CD. The role of oral dysbiosis in CD is poorly understood. We aimed to explore microbiome signature and dysbiosis of the salivary microbiome in CD patients, and correlate microbiota changes to the level of inflammation. Saliva samples were collected from healthy controls (HC) and CD patients (n = 40 per group). Salivary microbiome was analyzed by sequencing the entire 16S rRNA gene. Inflammatory biomarkers (C-reactive protein and calprotectin) were measured and correlated with microbiome diversity. Five dominant species were significantly enriched in CD, namely Veillonella dispar, Megasphaera stantonii, Prevotella jejuni, Dolosigranulum pigrum and Lactobacillus backii. Oral health had a significant impact on the microbiome since various significant features were cariogenic as Streptococcus mutans or periopathogenic such as Fusobacterium periodonticum. Furthermore, disease activity, duration and frequency of relapses impacted the oral microbiota. Treatment with monoclonal antibodies led to the emergence of a unique species called Simonsiella muelleri. Combining immunomodulatory agents with monoclonal antibodies significantly increased multiple pathogenic species such as Salmonella enterica, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Loss of diversity in CD was shown by multiple diversity indices. There was a significant negative correlation between gut inflammatory biomarkers (particularly calprotectin) and α-diversity, suggesting more inflammation associated with diversity loss in CD. Salivary dysbiosis was evident in CD patients, with unique microbiota signatures and perturbed species that can serve as disease biomarkers or potential targets for microbiota modulation. The interplay of various factors collectively contributed to dysbiosis, although each factor probably had a unique effect on the microbiome. The emergence of pathogenic bacteria in the oral cavity of CD patients is alarming since they can disturb gut homeostasis and induce inflammation by swallowing, or hematogenous spread of microbiota, their metabolites, or generated inflammatory mediators.

Project description:BackgroundMicrobiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes, but evidence from large human cohorts is lacking. This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations.MethodsWe included 1430 participants from the Guangzhou Nutrition and Health Study (GNHS) with both gut microbiome and cognitive assessment data available as a discovery cohort, of whom 272 individuals provided fecal samples twice before cognitive assessment. We selected 208 individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit. Fecal 16S rRNA and shotgun metagenomic sequencing, targeted serum metabolomics, and cytokine measurements were performed in the GNHS. The validation analyses were conducted in an Alzheimer's disease case-control study (replication study 1, n = 90) and another community-based cohort (replication study 2, n = 1300) with cross-sectional dataset.ResultsWe found protective associations of specific gut microbial genera (Odoribacter, Butyricimonas, and Bacteroides) with cognitive impairment in both the discovery cohort and the replication study 1. Result of Bacteroides was further validated in the replication study 2. Odoribacter was positively associated with hippocampal volume (β, 0.16; 95% CI 0.06-0.26, P = 0.002), which might be mediated by acetic acids. Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment. We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition.ConclusionsOur findings reveal that specific gut microbial features are closely associated with cognitive impairment and decreased hippocampal volume, which may play an important role in dementia development.

Dataset Information

Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment.

Publications

Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment.

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